A robust semi-supervised NMF model for single cell RNA-seq data
نویسندگان
چکیده
منابع مشابه
DropLasso: A robust variant of Lasso for single cell RNA-seq data
Single-cell RNA sequencing (scRNA-seq) is a fast growing approach to measure the genome-wide transcriptome of many individual cells in parallel, but results in noisy data with many dropout events. Existing methods to learn molecular signatures from bulk transcriptomic data may therefore not be adapted to scRNA-seq data, in order to automatically classify individual cells into predefined classes...
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The analysis of single-cell RNA-seq (scRNA-seq) data is complicated and biased by excess zero or near zero counts, the so-called dropouts due to the low amounts of mRNA sequenced within individual cells. We introduce scImpute, a statistical method to accurately and robustly impute the dropouts in scRNA-seq data. scImpute is shown as an effective tool to enhance the clustering of cell population...
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One of the most notable challenges in single cell RNA-Seq data analysis is the so called drop-out effect, where only a fraction of the transcriptome of each cell is captured. The random nature of drop-outs, however, makes it possible to consider imputation methods as means of correcting for drop-outs. In this paper we study some existing scRNA-Seq imputation methods and propose a novel iterativ...
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Introduction: The emergence of single-cell RNA-sequencing (scRNA-seq) technology has provided new information about the structure of cells, and provided data with very high resolution of the expression of different genes for each cell at a single time. One of the main uses of scRNA-seq is data clustering based on expressed genes, which sometimes leads to the detection of rare cell populations. ...
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ژورنال
عنوان ژورنال: PeerJ
سال: 2020
ISSN: 2167-8359
DOI: 10.7717/peerj.10091